In-class Exercise 4

Summary

• install packages
• import data and transform aspatial data to geospatial data: st_read(), read_rds(), st_as_sf()
• data Wrangling: sf -> Spatial class -> sp -> spatstat ppp format
• plot map

1. Install Packages

packages = c('maptools', 'sf', 'raster','spatstat', 'tmap','tidyverse')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}

2. Import data and transform aspatial data

sg_sf <- st_read("data/shapefile", layer="CostalOutline")

Reading layer `CostalOutline' from data source 
  `C:\yiling-yu\IS415_Blog\_posts\2021-09-06-in-class-exercise-4\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 60 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2663.926 ymin: 16357.98 xmax: 56047.79 ymax: 50244.03
Projected CRS: SVY21

mpsz_sf <- st_read(dsn = "data/shapefile", 
                layer = "MP14_SUBZONE_WEB_PL")

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\yiling-yu\IS415_Blog\_posts\2021-09-06-in-class-exercise-4\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

childcare <- read_rds("data/rds/childcare.rds")
CHAS <- read_rds("data/rds/CHAS.rds")

CHAS_sf <- st_as_sf(CHAS,
                    coords = c("X_COORDINATE",
                               "Y_COORDINATE"),
                    crs=3414)

childcare$Lat <- as.numeric(childcare$Lat)
childcare$Lng <- as.numeric(childcare$Lng)
childcare_sf <- st_as_sf(childcare,
                         coords = c("Lng",
                                    "Lat"),
                         crs=4326) %>%
  st_transform(crs =3414)

3. Data Wrangling

a. Converting from sf to Spatial class

childcare <- as_Spatial(childcare_sf)
CHAS <- as_Spatial(CHAS_sf)
mpsz <- as_Spatial(mpsz_sf)
sg <- as_Spatial(sg_sf)

b. Converting from Spatial class to sp format

childcare_sp <- as(childcare, "SpatialPoints")
CHAS_sp <- as(CHAS, "SpatialPoints")
sg_sp <- as(sg, "SpatialPolygons")

c. Converting from sp to spatstat ppp format

childcare_ppp <- as(childcare_sp, "ppp")
CHAS_ppp <- as(CHAS_sp, "ppp")

4. Plot map

tmap_mode('view')
tm_shape(childcare_sf) +
  tm_dots(alpha=0.4,
          col="blue",
          size=0.05) +
tm_shape(CHAS_sf) +
  tm_dots(alpha=0.4,
          col="red",
          size=0.05)